Kernel Principal Component Analysis
2016-08-23
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A kernel principal component analysis (PCA) was recently proposed as a nonlinear extension of a PCA. The basic
idea is to first map the input space into a feature space via nonlinear mapping and then compute the principal
components in that feature space. This letter adopts the kernel PCA as a mechanism for extracting facial
features. Through adopting a polynomial kernel, the principal components can be computed within the space
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